Most Android-powered devices have an accelerometer, and many now include a
gyroscope. The availability of the software-based sensors is more
variable because they often rely on one or more hardware sensors to derive their
data. Depending on the device, these software-based sensors can derive their
data either from the accelerometer and magnetometer or from the gyroscope.

Motion sensors are useful for monitoring device movement, such as tilt, shake, rotation, or
swing. The movement is usually a reflection of direct user input (for example, a user steering a
car in a game or a user controlling a ball in a game), but it can also be a reflection of the
physical environment in which the device is sitting (for example, moving with you while you drive
your car). In the first case, you are monitoring motion relative to the device's frame of reference
or your application's frame of reference; in the second case you are monitoring motion relative to
the world's frame of reference. Motion sensors by themselves are not typically used to monitor
device position, but they can be used with other sensors, such as the geomagnetic field sensor, to
determine a device's position relative to the world's frame of reference (see Position Sensors for more
information).

All of the motion sensors return multi-dimensional arrays of sensor values for each SensorEvent. For example, during a single sensor event the accelerometer returns
acceleration force data for the three coordinate axes, and the gyroscope returns rate of rotation
data for the three coordinate axes. These data values are returned in a float array
(values) along with other SensorEvent
parameters. Table 1 summarizes the motion sensors that are available on the Android platform.

The rotation vector sensor and the gravity sensor are the most frequently used sensors for motion
detection and monitoring. The rotational vector sensor is particularly versatile and can be used for
a wide range of motion-related tasks, such as detecting gestures, monitoring angular change, and
monitoring relative orientation changes. For example, the rotational vector sensor is ideal if you
are developing a game, an augmented reality application, a 2-dimensional or 3-dimensional compass,
or a camera stabilization app. In most cases, using these sensors is a better choice than using
the accelerometer and geomagnetic field sensor or the orientation sensor.

Android Open Source Project sensors

The Android Open Source Project (AOSP) provides three software-based motion sensors: a gravity
sensor, a linear acceleration sensor, and a rotation vector sensor. These sensors were updated in
Android 4.0 and now use a device's gyroscope (in addition to other sensors) to improve stability and
performance. If you want to try these sensors, you can identify them by using the getVendor() method and the getVersion() method
(the vendor is Google LLC; the version number is 3). Identifying these sensors by vendor and
version number is necessary because the Android system considers these three sensors to be secondary
sensors. For example, if a device manufacturer provides their own gravity sensor, then the AOSP
gravity sensor shows up as a secondary gravity sensor. All three of these sensors rely on a
gyroscope: if a device does not have a gyroscope, these sensors do not show up and are not
available for use.

Use the gravity sensor

The gravity sensor provides a three dimensional vector indicating the
direction and magnitude of gravity. Typically, this sensor is used to determine
the device's relative orientation in space. The following code shows you how to
get an instance of the default gravity sensor:

Java

The units are the same as those used by the acceleration
sensor (m/s2), and the coordinate system is the same as the one used by the
acceleration sensor.

Note: When a device is at rest, the output of the gravity sensor
should be identical to that of the accelerometer.

Use the linear accelerometer

The linear acceleration sensor provides you with a three-dimensional vector
representing acceleration along each device axis, excluding gravity. You can use
this value to perform gesture detection. The value can also serve as input to an
inertial navigation system, which uses dead reckoning. The following code shows
you how to get an instance of the default linear acceleration sensor:

Conceptually, this sensor provides you with acceleration data according to the following
relationship:

linear acceleration = acceleration - acceleration due to gravity

You typically use this sensor when you want to obtain acceleration data without the influence of
gravity. For example, you could use this sensor to see how fast your car is going. The linear
acceleration sensor always has an offset, which you need to remove. The simplest way to do this is
to build a calibration step into your application. During calibration you can ask the user to set
the device on a table, and then read the offsets for all three axes. You can then subtract that
offset from the acceleration sensor's direct readings to get the actual linear
acceleration.

The sensor coordinate
system is the same as the one used by the acceleration sensor, as are the units of measure
(m/s2).

Use the rotation vector sensor

The rotation vector represents the orientation of the device as a combination of an angle and an
axis, in which the device has rotated through an angle θ around an axis (x, y, or z). The following
code shows you how to get an instance of the default rotation vector sensor:

Where the magnitude of the rotation vector is equal to sin(θ/2), and the direction of the
rotation vector is equal to the direction of the axis of rotation.

Figure 1. Coordinate system used by the rotation vector sensor.

The three elements of the rotation vector are equal to the last three components of a unit
quaternion (cos(θ/2), x*sin(θ/2), y*sin(θ/2), z*sin(θ/2)). Elements of the rotation vector are
unitless. The x, y, and z axes are defined in the same way as the acceleration sensor. The reference
coordinate system is defined as a direct orthonormal basis (see figure 1). This coordinate system
has the following characteristics:

X is defined as the vector product Y x Z. It is tangential to the
ground at the device's current location and points approximately East.

Y is tangential to the ground at the device's current location and points toward the
geomagnetic
North Pole.

Use the significant motion sensor

The significant motion sensor triggers an event each time significant motion is detected and
then it disables itself. A significant motion is a motion that might lead to a change in the
user's location; for example walking, biking, or sitting in a moving car. The following code shows you
how to get an instance of the default significant motion sensor and how to register an event
listener:

Use the step counter sensor

The step counter sensor provides the number of steps taken by the user since the last reboot
while the sensor was activated. The step counter has more latency (up to 10 seconds) but more
accuracy than the step detector sensor.

Note: You must declare the
ACTIVITY_RECOGNITION
permission in order for your app to use this sensor on devices running
Android 10 (API level 29) or higher.

The following code shows you how to get an instance of the default step
counter sensor:

To preserve the battery on devices running your app, you should use the
JobScheduler class to retrieve the current value from
the step counter sensor at a specific interval. Although different types of apps
require different sensor-reading intervals, you should make this interval as
long as possible unless your app requires real-time data from the sensor.

Use the step detector sensor

The step detector sensor triggers an event each time the user takes a step. The latency is
expected to be below 2 seconds.

Note: You must declare the
ACTIVITY_RECOGNITION
permission in order for your app to use this sensor on devices running
Android 10 (API level 29) or higher.

The following code shows you how to get an instance of the default step
detector sensor:

Work with raw data

The following sensors provide your app with raw data about the linear and
rotational forces being applied to the device. In order to use the values from
these sensors effectively, you need to filter out factors from the environment,
such as gravity. You might also need to apply a smoothing algorithm to the trend
of values to reduce noise.

Use the accelerometer

An acceleration sensor measures the acceleration applied to the device, including the force of
gravity. The following code shows you how to get an instance of the default acceleration sensor:

Conceptually, an acceleration sensor determines the acceleration that is applied
to a device (Ad) by measuring the forces that are applied to the sensor
itself (Fs) using the following relationship:

However, the force of gravity is always influencing the measured acceleration according to
the following relationship:

For this reason, when the device is sitting on a table (and not accelerating), the
accelerometer reads a magnitude of g = 9.81 m/s2. Similarly, when the device is in
free fall and therefore rapidly accelerating toward the ground at 9.81 m/s2, its
accelerometer reads a magnitude of g = 0 m/s2. Therefore, to measure
the real acceleration of the device, the contribution of the force of gravity must be removed from
the accelerometer data. This can be achieved by applying a high-pass filter. Conversely, a low-pass
filter can be used to isolate the force of gravity. The following example shows how you can do
this:

Note: You can use many different techniques to filter sensor data.
The code sample above uses a simple filter constant (alpha) to create a low-pass filter. This filter
constant is derived from a time constant (t), which is a rough representation of the latency that
the filter adds to the sensor events, and the sensor's event delivery rate (dt). The code sample
uses an alpha value of 0.8 for demonstration purposes. If you use this filtering method you may need
to choose a different alpha value.

Accelerometers use the standard sensor coordinate
system. In practice, this means that the following conditions apply when a device is laying
flat on a table in its natural orientation:

If you push the device on the left side (so it moves to the right), the x acceleration value
is positive.

If you push the device on the bottom (so it moves away from you), the y acceleration value is
positive.

If you push the device toward the sky with an acceleration of A m/s2, the
z acceleration value is equal to A + 9.81, which corresponds to the acceleration of the device (+A
m/s2) minus the force of gravity (-9.81 m/s2).

The stationary device will have an acceleration value of +9.81, which corresponds to the
acceleration of the device (0 m/s2 minus the force of gravity, which is -9.81
m/s2).

In general, the accelerometer is a good sensor to use if you are monitoring device motion.
Almost every Android-powered handset and tablet has an accelerometer, and it uses about 10 times
less power than the other motion sensors. One drawback is that you might have to implement
low-pass and high-pass filters to eliminate gravitational forces and reduce noise.

The Android SDK provides a sample application that shows how to use the acceleration sensor (Accelerometer
Play).

Use the gyroscope

The gyroscope measures the rate of rotation in rad/s around a device's x, y,
and z axis. The following code shows you how to get an instance of the default gyroscope:

The sensor's coordinate system
is the same as the one used for the acceleration sensor. Rotation is positive in the
counter-clockwise direction; that is, an observer looking
from some positive location on the x, y or z axis at a device positioned on the origin would report
positive rotation if the device appeared to be rotating counter clockwise. This is the
standard mathematical definition of positive rotation and is not the same as the definition for
roll that is used by the orientation sensor.

Usually, the output of the gyroscope is integrated over time to calculate a rotation describing
the change of angles over the timestep. For example:

Standard gyroscopes provide raw rotational data without any filtering or correction for noise and
drift (bias). In practice, gyroscope noise and drift will introduce errors that need to be
compensated for. You usually determine the drift (bias) and noise by monitoring other sensors, such
as the gravity sensor or accelerometer.

Use the uncalibrated gyroscope

The uncalibrated gyroscope is similar to the gyroscope,
except that no gyro-drift compensation is applied to the rate of rotation. Factory calibration
and temperature compensation are still applied to the rate of rotation. The uncalibrated
gyroscope is useful for post-processing and melding orientation data. In general,
gyroscope_event.values[0] will be close to
uncalibrated_gyroscope_event.values[0] - uncalibrated_gyroscope_event.values[3].
That is,

calibrated_x ~= uncalibrated_x - bias_estimate_x

Note: Uncalibrated sensors provide more raw results and may
include some bias, but their measurements contain fewer jumps from corrections applied through
calibration. Some applications may prefer these uncalibrated results as smoother and more
reliable. For instance, if an application is attempting to conduct its own sensor fusion,
introducing calibrations can actually distort results.

In addition to the rates of rotation, the uncalibrated gyroscope also provides the estimated
drift around each axis. The following code shows you how to get an instance of the default
uncalibrated gyroscope: